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Conversational, Longitudinal, Ecological Assessment (CLEA): Exploring a new AI-driven method for qualitative data collection in a behavioural health context

Samuel Downes*, Thomas Krys, Kenton O'Hara, Max Western, Lauren B P Thompson, Amberly L C Brigden

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

In this paper, we present conversational longitudinal ecological assessment (CLEA), a novel conversational AI–enabled method for collecting ecologically valid, temporally sensitive qualitative health data via mobile instant messaging. We report findings from an exploratory deployment of an instantiation of CLEA within a 12-week community-based weight management programme, delivered by a charity partner in an area of deprivation. Using WhatsApp, we deployed our CLEA chat-agent to conduct twice-weekly conversational data collection sessions with participants, to elicit data about their experience of the programme and associated behaviour change. This was followed by in-person semi-structured interviews (N = 9) to examine user experiences and perceptions of interacting with the chat-agent. Participants reported that WhatsApp’s familiarity supported accessibility and sustained engagement, while the conversational format encouraged reflection directed towards the research focus. Responding to chat-agent prompts required cognitive effort, leading some participants to defer engagement until they had adequate time and mental space; however, this reflective demand was largely experienced as beneficial within the programme context. The AI’s quasi-human interactional qualities fostered a sense of support while reducing social judgement, enabling more candid disclosure. Together, these findings suggest initial feasibility and acceptability of this CLEA implementation within a community-based programme in an area of deprivation. Further, while the responses in single messages were often brief, useful, relevant, and meaningful insights appeared to develop over the course of conversational sessions. The study highlights both the opportunities and trade-offs of conversational AI for qualitative data collection, including design implications for health researchers looking to implement or extend the method. Finally, we position CLEA in relation to other longitudinal methods of qualitative health data elicitation.
Original languageEnglish
Article numbere0001216
Number of pages23
JournalPLOS Digital Health
Volume5
Issue number5
DOIs
Publication statusPublished - 27 May 2026

Bibliographical note

Publisher Copyright:
© 2026 Downes et al.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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